Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

58.6K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
58.6K
Mutations in Microorganisms01:18

Mutations in Microorganisms

33
Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
33
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.2K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.2K
Viral Mutations00:36

Viral Mutations

32.5K
A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
32.5K
Genetic Drift03:33

Genetic Drift

39.9K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
39.9K
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

12.1K
Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
12.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A pan-cancer single-cell analysis of intratumoral copy number diversity and evolution.

Cancer discovery·2026
Same author

Age distinguishes selection from causation in cancer genomes.

Nature genetics·2026
Same author

Age distinguishes selection from causation in cancer genomes.

bioRxiv : the preprint server for biology·2025
Same author

Mathematical Modeling Quantifies "Just-Right" APC Inactivation for Colorectal Cancer Initiation.

Cancer research·2025
Same author

Error-induced extinction in a multi-type critical birth-death process.

Journal of mathematical biology·2024
Same author

Strand-resolved mutagenicity of DNA damage and repair.

Nature·2024
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Jul 24, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.0K

Sequential mutations in exponentially growing populations.

Michael D Nicholson1, David Cheek2, Tibor Antal3

  • 1Edinburgh Cancer Research, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom.

Plos Computational Biology
|July 10, 2023
PubMed
Summary
This summary is machine-generated.

This study models cancer and bacterial evolution using stochastic processes. It reveals that the number and arrival time of cells with n mutations follow specific distributions, regardless of mutation type.

More Related Videos

Measuring Microbial Mutation Rates with the Fluctuation Assay
07:44

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

23.7K
Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model
08:46

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Published on: September 29, 2011

15.7K

Related Experiment Videos

Last Updated: Jul 24, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.0K
Measuring Microbial Mutation Rates with the Fluctuation Assay
07:44

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

23.7K
Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model
08:46

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Published on: September 29, 2011

15.7K

Area of Science:

  • Evolutionary biology
  • Mathematical biology
  • Genetics

Background:

  • Stochastic models are crucial for understanding cancer and bacterial evolution, particularly for tracking sequential mutation acquisition.
  • Key questions involve determining the number of cells with specific mutations and their appearance time, especially in exponentially growing populations.
  • Previous models have addressed these questions only in limited scenarios.

Purpose of the Study:

  • To develop a general framework for modeling sequential mutation acquisition in evolving populations.
  • To derive probability distributions for the number and arrival time of cells with n mutations under broad conditions.
  • To provide a method for assessing the impact of demographic and mutational rates on mutant cell emergence.

Main Methods:

  • Utilizing a multitype branching process framework to model population growth and mutation.
  • Analyzing biologically relevant limiting regimes characterized by large times and small mutation rates.
  • Deriving analytical probability distributions for cell counts and their appearance times.

Main Results:

  • The number of cells with n mutations follows a Mittag-Leffler distribution.
  • The arrival time of cells with n mutations follows a logistic distribution.
  • These distributions hold true irrespective of the number of mutations (n) or their selective effects (advantageous, neutral, or deleterious).

Conclusions:

  • The derived distributions offer a generalized solution for predicting mutant cell dynamics in evolving populations.
  • The findings enable rapid assessment of how changes in division, death, and mutation rates influence the emergence of mutant cells.
  • Results have implications for improving mutation rate inference methods, such as those used in fluctuation assays.